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BIFOLD-BigEarthNetv2-0/
resnet101-all-v0.1.1

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TU Berlin	RSiM	DIMA	BigEarth	BIFOLD
TU Berlin Logo	RSiM Logo	DIMA Logo	BigEarth Logo	BIFOLD Logo

Resnet101 pretrained on BigEarthNet v2.0 using Sentinel-1 & Sentinel-2 bands

NOTE: This version of the model has been trained with a different band order that is not compatible with the newer versions and does not match the order proposed in the technical documentation of Sentinel-2.

The following bands (in the specified order) were used to train the models with version 0.1.1:

For models using Sentinel-1 only: Sentinel-1 bands ["VH", "VV"]

For models using Sentinel-2 only: Sentinel-2 10m bands and 20m bands ["B02", "B03", "B04", "B08", "B05", "B06", "B07", "B11", "B12", "B8A"]

For models using Sentinel-1 and Sentinel-2: Sentinel-2 10m bands and 20m bands and Sentinel-1 bands = ["B02", "B03", "B04", "B08", "B05", "B06", "B07", "B11", "B12", "B8A", "VH", "VV"]

Newer models are compatible with the order in the technical documentation of Sentinel-2 and were trained with the following band order:

For models using Sentinel-1 only: Sentinel-1 bands ["VV", "VH"]

For models using Sentinel-2 only: Sentinel-2 10m bands and 20m bands ["B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12"]

For models using Sentinel-1 and Sentinel-2: Sentinel-1 bands and Sentinel-2 10m bands and 20m bands ["VV", "VH", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12"]

This model was trained on the BigEarthNet v2.0 (also known as reBEN) dataset using the Sentinel-1 & Sentinel-2 bands. It was trained using the following parameters:

Number of epochs: up to 100 (with early stopping after 5 epochs of no improvement based on validation average precision macro)
Batch size: 512
Learning rate: 0.001
Dropout rate: 0.15
Drop Path rate: 0.15
Learning rate scheduler: LinearWarmupCosineAnnealing for 1000 warmup steps
Optimizer: AdamW
Seed: 0

The weights published in this model card were obtained after 20 training epochs. For more information, please visit the official BigEarthNet v2.0 (reBEN) repository, where you can find the training scripts.

The model was evaluated on the test set of the BigEarthNet v2.0 dataset with the following results:

Metric	Macro	Micro
Average Precision	0.716136	0.863446
F1 Score	0.660243	0.768155
Precision	0.716136	0.863446

Example

A Sentinel-2 image (true color representation)
BigEarthNet

Class labels	Predicted scores
Agro-forestry areas Arable land Beaches, dunes, sands ... Urban fabric	0.000000 0.000000 0.000000 ... 0.000000

To use the model, download the codes that define the model architecture from the official BigEarthNet v2.0 (reBEN) repository and load the model using the code below. Note that you have to install configilm to use the provided code.

python

from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier

model = BigEarthNetv2_0_ImageClassifier.from_pretrained("path_to/huggingface_model_folder")

e.g.

python

from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier

model = BigEarthNetv2_0_ImageClassifier.from_pretrained(
  "BIFOLD-BigEarthNetv2-0/resnet101-all-v0.1.1")

If you use this model in your research or the provided code, please cite the following papers:

bibtex

@article{clasen2024refinedbigearthnet,
  title={reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis}, 
  author={Clasen, Kai Norman and Hackel, Leonard and Burgert, Tom and Sumbul, Gencer and Demir, Beg{\"u}m and Markl, Volker},
  year={2024},
  eprint={2407.03653},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2407.03653}, 
}

bibtex

@article{hackel2024configilm,
  title={ConfigILM: A general purpose configurable library for combining image and language models for visual question answering},
  author={Hackel, Leonard and Clasen, Kai Norman and Demir, Beg{\"u}m},
  journal={SoftwareX},
  volume={26},
  pages={101731},
  year={2024},
  publisher={Elsevier}
}